Kensho Technologies was focused on analyzing North Korean missile launches, earthquakes and elections when John van Moyland joined in 2014. The Central Intelligence Agency was among its early backers.
Now, its focus has shifted to finance. S&P Global Inc. bought the firm last year and the artificial-intelligence startup lasered in on another technical challenge: developing the next generation of indexed funds.
According to van Moyland, machines are ready to design better indexes to underpin investment vehicles passively managing $7.3 trillion in the U.S.
“We’re doing what a lot of research shops have done with humans in the past — and doing it at scale, in a highly predictable, highly automated, efficient way,” van Moyland, managing director and global head of S&P Kensho Indices, said in an interview. “Why would you ever limit yourself to aged financial data when there’s a sea of information out there?”
The race is on to create robotic ETFs — a bet that human investors would rather trust investment vehicles designed with far-flung data digested with natural-language processing, machine learning and AI.
Goldman Sachs Group Inc. — which along with Google Ventures also backed Kensho in its early years — launched a series of exchange-traded funds that track indexes designed by machines. BlackRock Inc. also offers some bot-built products.
With more than 2,000 ETFs in the U.S. alone, managers must battle to stand out from the crowd. More urgently, fund issuers and indexers need specialized products that can yield higher fees as passive investing takes a bite out of revenue.
While a broad-market stock ETF generates fees of as little as 20 cents for every $1,000 invested, AI-designed ETFs range from $1.80 to $8.
Kensho’s machines are helping S&P develop indexes with advanced methods that identify relevant stocks. The bots capture all the ways an industry is described — searching for references to self-driving cars as well as automated vehicles, for example — while adding related industries such as lithium batteries in this instance.
Among its creations is the Final Frontiers Index of companies involved in exploration of deep space and the ocean depths.
Once programmers have a universe of securities, they use natural-language processing to understand context, confirming that references are to new products or services rather than risks, for example. That lets them weight the index accordingly.